May Ai? Design ideation with cooperative contextual bandits

Janin Koch, Andrés Lucero, Lena Hegemann, Antti Oulasvirta

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

60 Citations (Scopus)
282 Downloads (Pure)


Design ideation is a prime creative activity in design. However, it is challenging to support computationally due to its quickly evolving and exploratory nature. The paper presents cooperative contextual bandits (CCB) as a machine-learning method for interactive ideation support. A CCB can learn to propose domain-relevant contributions and adapt their exploration/exploitation strategy. We developed a CCB for an interactive design ideation tool that 1) suggests inspirational and situationally relevant materials (“may AI?”); 2) explores and exploits inspirational materials with the designer; and 3) explains its suggestions to aid reflection. The application case of digital mood board design is presented, wherein visual inspirational materials are collected and curated in collages. In a controlled study, 14 of 16 professional designers preferred the CCB-augmented tool. The CCB approach holds promise for ideation activities wherein adaptive and steerable support is welcome but designers must retain full outcome control.

Original languageEnglish
Title of host publicationCHI 2019 - Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
ISBN (Electronic)9781450359702
Publication statusPublished - 2 May 2019
MoE publication typeA4 Conference publication
EventACM SIGCHI Annual Conference on Human Factors in Computing Systems - Glasgow, United Kingdom
Duration: 4 May 20199 May 2019


ConferenceACM SIGCHI Annual Conference on Human Factors in Computing Systems
Abbreviated titleACM CHI
Country/TerritoryUnited Kingdom
Internet address


  • Creativity support tools
  • Ideation support
  • Interactive machine-learning
  • Mood board design


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  • COMPUTED: Computational User Interface Design

    Feit, A., Oulasvirta, A., Todi, K., Dayama, N., Koch, J., Nancel, M., Brückner, L., Liao, Y., Shiripour, M., Leiva, L., Kim, S. & Nioche, A.


    Project: EU: ERC grants

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